© Marcel Burkhardt
Bayesian population analysis using WinBUGS
A hierarchical perspective
- Has been written for ecologists by two well-known population ecologists.
- Contains analyses of simulated data, along with fully commented R code for the generation of these data sets, as well as analyses of real data.
- Contains exercises for each chapter, with solutions provided on this web-site.
- Integrates with program R—all analyses are conducted by calling WinBUGS from R.
- Illustrates the tremendous modeling freedom given to ecologists when using the simple and flexible BUGS language.
- Greatly enhances your understanding of key statistical concepts, such as linear and generalized linear models, random effects and hierarchical models.
Marc Kéry and Michael Schaub are population ecologists with the Swiss Ornithological Institute. Together, they have authored over 120 peer-reviewed journal articles on a wide range of topics, including the analysis of large-scale monitoring programs, demographic population analyses, experimental design for animal and plant surveys, and the population ecology of rare species, as well Introduction to WinBUGS for Ecologists (Academic Press, 2010).
Table of contents
2. Brief introduction to Bayesian statistical modeling
3. Introduction to the generalized linear model (GLM): The simplest model for count data
4. Introduction to random effects: Conventional Poisson GLMM for count data.
5. State-space models for population counts
6. Estimation of the size of a closed population from capture-recapture data
7. Estimation of survival from capture-recapture data using the Cormack-Jolly-Seber (CJS) model
8. Estimation of survival using mark-recovery data
9. Estimation of survival and movement from capture-recapture data using multistate models
10. Estimation of survival, recruitment and population size from capture-recapture data using the Jolly-Seber (JS) model
11. Estimation of demographic rates, population size and projection matrices from multiple data types using integrated population models
12. Estimation of abundance from counts in metapopulation designs using the binomial mixture model
13. Estimation of occurrence and species distribution from detection/nondetection data in metapopulation designs using site-occupancy models
14. Concluding remarks
Appendix 1: A list of WinBUGS tricks
Appendix 2: Some further useful multistate capture-recapture models
Web appendix 1: Utility functions
Web appendix 2: Code to simulate data for the integrated population model
This webpage contains supplementary documents to the book and workshop announcements